# Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection

arXiv: https://arxiv.org/abs/2305.18216 · HTML: https://mlsanity.com/paper/2305.18216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 11 of 11)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Deep Face Representations for Differential Morphing Attack Detection](https://mlsanity.com/paper/2001.01202/) (2020)
- [Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection](https://mlsanity.com/paper/2007.03621/) (2020)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [Face morphing detection in the presence of printing/scanning and heterogeneous image sources](https://mlsanity.com/paper/1901.08811/) (2019)
- [On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection](https://mlsanity.com/paper/2007.02684/) (2020)
- [Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information](https://mlsanity.com/paper/2110.13552/) (2021)
- [Deep Composite Face Image Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2211.11039/) (2022)

## Built on by (most cited first, 4 of 4)

- [SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples](https://mlsanity.com/paper/2409.05595/) (2024)
- [Dealing with Subject Similarity in Differential Morphing Attack Detection](https://mlsanity.com/paper/2404.07667/) (2024)
- [WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation](https://mlsanity.com/paper/2507.12493/) (2025)
- [DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection](https://mlsanity.com/paper/2604.17961/) (2026)

## Abstract (arXiv)

Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because it enables both individuals involved to exploit the same document. In this study, face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack generation and detecting potential Morphing Attacks. We build upon previous embedding studies in both use cases using the MagFace model. For the first objective, we employ an pre-selection algorithm that pairs individuals based on face embedding similarity. We quantify the attack potential of differently morphed face images to compare the usability of pre-selection in automatically generating numerous successful Morphing Attacks. Regarding the second objective, we compare embeddings from two state-of-the-art face recognition systems in terms of their ability to detect Morphing Attacks. Our findings demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Both open-source and COTS face recognition systems are susceptible to generated attacks, particularly when pre-selection is based on embeddings rather than random pairing which was only constrained by soft biometrics. More accurate face recognition systems exhibit greater vulnerability to attacks, with COTS systems being the most susceptible. Additionally, MagFace embeddings serve as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the advantages of face embeddings in more effective image pre-selection for face morphing and accurate detection of morphed face images. This is supported by extensive analysis of various designed attacks. The MagFace model proves to be a powerful alternative to the commonly used ArcFace model for both objectives, pre-selection and attack detection.